Do you really mean a jackknife or leave-one-out crossvalidation? They are not the same, but the second is often incorrectly called the first.
In either case, I would point you at the book the 'boot' package supports. See for example its cv.glm function. On Mon, 7 Nov 2005, Jeffrey Stratford wrote: > Thanks for the help with the hier.part analysis. All the problems > stemmed from an import problem which was solved with file.chose(). > > Now that I have the variables that I'd like to use I need to run some > GLM models. I think I have that part under control but I'd like to use > a jackknife approach to model validation (I was using a hold out sample > but this seems to have fallen out of favor). > > I'd appreciate it if someone could just point me in the right direction > for the jackkife analysis given a particular distribution, coefficients, > etc. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
